{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import multivariate_normal   ###multivariatenormal###\n",
    "\n",
    "irlidar = multivariate_normal(mean=np.array([19.86,729.3]).T, cov=np.array([[42, -10],[-10, 17.7]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np                ###contour###\n",
    " \n",
    "x, y = np.mgrid[0:40, 710:750]     #2次元平面に均等にX座標、Y座標を作る\n",
    "pos = np.empty(x.shape + (2,))     #xは40x40の2次元リストで、これに3次元目を加えて40x40x2のリストを作成\n",
    "pos[:, :, 0] = x                                   #加えた3次元目にx,yを代入\n",
    "pos[:, :, 1] = y\n",
    "cont = plt.contour(x, y, irlidar.pdf(pos)) #x, y座標と、それに対応する密度を算出\n",
    "cont.clabel(fmt='%1.1e')                         #等高線に値を書き込むためのフォーマット指定\n",
    "\n",
    "plt.show()   #描画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
